Journal
MOLECULAR INFORMATICS
Volume 30, Issue 9, Pages 817-826Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/minf.201100059
Keywords
Kernel-based learning; Confidence estimation; Domain of applicability; QSAR; QSPR
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Funding
- European Community, under the PASCAL2 Network of Excellence [ICT-216886]
- DFG [MU 987/4-2]
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Statistical models are frequently used to estimate molecular properties, e.g., to establish quantitative structure-activity and structure-property relationships. For such models, interpretability, knowledge of the domain of applicability, and an estimate of confidence in the predictions are essential. We develop and validate a method for the interpretation of kernel-based prediction models. As a consequence of interpretability, the method helps to assess the domain of applicability of a model, to judge the reliability of a prediction, and to determine relevant molecular features. Increased interpretability also facilitates the acceptance of such models. Our method is based on visualization: For each prediction, the most contributing training samples are computed and visualized. We quantitatively show the effectiveness of our approach by conducting a questionnaire study with 71 participants, resulting in significant improvements of the participants' ability to distinguish between correct and incorrect predictions of a Gaussian process model for Ames mutagenicity.
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